Collaborative perception by leveraging the shared semantic information plays a crucial role in overcoming the individual limitations of isolated agents. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension. Consequently, the potential benefits of collaboration remain underutilized. In this article, we propose Select2Col, a novel collaborative perception framework that takes into account the s patial-t e mpora l importanc e of semanti c informa t ion. Within the Select2Col, we develop a collaborator selection method that utilizes a lightweight graph neural network (GNN) to estimate the importance of semantic information (IoSI) of each collaborator in enhancing perception performance, thereby identifying contributive collaborators while excluding those that potentially bring negative impact. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short-term attention modules to capture the IoSI in feature representation from the spatial and temporal dimensions respectively, and assigns IoSI-consistent weights for efficient fusion of information from selected collaborators. Extensive experiments on three open datasets demonstrate that our proposed Select2Col significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/ZJUNICE/Select2Col/ .

Correntropy has been proved to be effective in eliminating the adverse effects of impulsive noises in adaptive filtering. However, correntropy is not desirable when the error between the two random variables is asymmetrically distributed around zero. To address this problem, asymmetric correntropy using an asymmetric Gaussian function as the kernel function was proposed. However, an asymmetric Gaussian function is not always the best choice and can be further expanded. In this paper, we propose a robust adaptive filtering based on a more flexible definition of asymmetric correntropy, which is called generalized asymmetric correntropy that adopts a generalized asymmetric Gaussian density (GAGD) function as the kernel. With the shape parameter properly selected, the generalized asymmetric correntropy may get better performance than the original asymmetric correntropy. The steady-state performance of the adaptive filter based on the generalized maximum asymmetric correntropy criterion (GMACC) is theoretically studied and verified by simulation experiments. The asymmetric characteristics of queue delay in satellite networks is analyzed and described, and the proposed algorithm is used to predict network delay, which is essential in space telemetry. Simulation results demonstrate the desirable performance of the new algorithm.

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming (MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

Traditional collaborative perception methods typically focus on optimizing perception performance in ideal wireless transmission conditions. However, in real-world constrained networks, it is crucial for collaborative perception schemes to alleviate network load while ensuring performance robustness. To address this challenge, this letter introduces S2CP, a Semantic Communication empowered Collaborative Perception framework. Within the S2CP, we propose the Multi-scale Dilated Cross-Attention (MDCA) module to effectively extract task-oriented valuable semantic features for transmission, thereby minimizing data transmission overhead and improving perception performance. Furthermore, to mitigate feature distortion during wireless transmission, we develop a pre-training strategy utilizing Masked AutoEncoders (MAE) to enhance the robustness of S2CP. Experimental results demonstrate that S2CP significantly enhances perception performance while substantially reducing network transmission volume.

In the fifth-generation communication system, millimeter wave (mmWave) networks will coexist with traditional micro wave ( $\mu $ Wave) networks, which allows for higher data transmission rate and better user experience. In this paper, we give a comprehensive framework of mathematical models and analytical methods for multi-tier mmWave and $\mu $ Wave hybrid caching networks based on stochastic geometry. We propose an association strategy by assuming average biased-received power association and Rayleigh fading. Accordingly, by using a D-ball approximating blockage model of mmWave networks, expressions of the cell association probability and the coverage probability of the hybrid networks are derived. Also, we use the average successful delivery probability as the performance metric to analyze the existing caching placement strategies. Simulation results validate the accuracy of our analytical conclusions.